{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mon Jun  5 00:05:19 2023       \n",
      "+---------------------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 530.51.01              Driver Version: 532.03       CUDA Version: 12.1     |\n",
      "|-----------------------------------------+----------------------+----------------------+\n",
      "| GPU  Name                  Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf            Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                                         |                      |               MIG M. |\n",
      "|=========================================+======================+======================|\n",
      "|   0  NVIDIA GeForce RTX 3090         On | 00000000:09:00.0  On |                  N/A |\n",
      "| 97%   51C    P2              154W / 350W|  11611MiB / 24576MiB |      2%      Default |\n",
      "|                                         |                      |                  N/A |\n",
      "+-----------------------------------------+----------------------+----------------------+\n",
      "                                                                                         \n",
      "+---------------------------------------------------------------------------------------+\n",
      "| Processes:                                                                            |\n",
      "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
      "|        ID   ID                                                             Usage      |\n",
      "|=======================================================================================|\n",
      "|    0   N/A  N/A        22      G   /Xwayland                                 N/A      |\n",
      "|    0   N/A  N/A        33      G   /Xwayland                                 N/A      |\n",
      "+---------------------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.utils.data import Dataset\n",
    "from torchvision import datasets, transforms\n",
    "from PIL import Image\n",
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "use_cuda = torch.cuda.is_available()\n",
    "device = torch.device(\"cuda\" if use_cuda else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Flatten(nn.Module):\n",
    "    def forward(self, x): return x.view(x.size(0), -1)\n",
    "\n",
    "class Net(nn.Sequential):\n",
    "    def __init__(self, i):\n",
    "        out_size = [246016, 57600, 12544, 2304, 256]\n",
    "        super().__init__(\n",
    "            nn.Conv2d(3, 32, 3, 1), nn.ReLU(),\n",
    "            *[layer for _ in range(i) for layer in [nn.Conv2d(32, 32, 3, 1), nn.MaxPool2d(2), nn.Dropout(0.25)]],\n",
    "            nn.Conv2d(32, 64, 3, 1), nn.MaxPool2d(2), nn.Dropout(0.25),\n",
    "            Flatten(), nn.Linear(out_size[i], 128), nn.ReLU(), nn.Dropout(0.5),\n",
    "            nn.Linear(128, 2), nn.LogSoftmax(dim=1) )\n",
    "\n",
    "def train(model, device, train_loader, optimizer, epoch):\n",
    "    model.train()\n",
    "    losses = []\n",
    "    for batch_idx, (data, target) in enumerate(train_loader):\n",
    "        data, target = data.to(device), target.to(device)\n",
    "        optimizer.zero_grad()\n",
    "        output = model(data)\n",
    "        loss = F.nll_loss(output, target)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        # if batch_idx % 100 == 0:\n",
    "        #     print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
    "        #         epoch, batch_idx*len(data), len(train_loader.dataset),\n",
    "        #         100. * batch_idx/len(train_loader), loss.item()))\n",
    "        losses.append(loss.item())\n",
    "    return sum(losses)/len(losses)\n",
    "\n",
    "from sklearn.metrics import recall_score\n",
    "\n",
    "def test(model, device, test_loader):\n",
    "    model.eval()\n",
    "    test_loss = 0\n",
    "    all_preds = []\n",
    "    all_targets = []\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        for data, target in test_loader:\n",
    "            data, target = data.to(device), target.to(device)\n",
    "            output = model(data)\n",
    "            test_loss += F.nll_loss(output, target, reduction='sum').item()\n",
    "            pred = output.argmax(dim=1, keepdim=True)\n",
    "            \n",
    "            # Store predictions and targets for recall calculation\n",
    "            all_preds.extend(pred.cpu().numpy())\n",
    "            all_targets.extend(target.cpu().numpy())\n",
    "    \n",
    "    recalls = recall_score(all_targets, all_preds, average=None)  # compute recall for each class\n",
    "    class_recall = recalls[1]  # replace 0 with the index of the class you're interested in\n",
    "\n",
    "    # print('\\nTest set: Average loss: {:.4f}, Recall: {:.2f}\\n'.format(\n",
    "    #     test_loss/len(test_loader.dataset), class_recall))\n",
    "\n",
    "    return class_recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MyDataset(Dataset):\n",
    "    def __init__(self, data_dir, transforms=None):\n",
    "        self.data_dir = data_dir\n",
    "        self.data_info = self.get_img_info(data_dir)\n",
    "        self.transforms = transforms\n",
    "\n",
    "    def __getitem__(self, item):\n",
    "        path_img, label = self.data_info.iloc[item][1:3]\n",
    "        label = int(label)\n",
    "        path_img = os.path.join(self.data_dir, path_img)\n",
    "        image = Image.open(path_img).convert('RGB') # Gray scale is enough for logic interpretation\n",
    "        # 使用定义好的transforms，对数据进行处理\n",
    "        if self.transforms is not None:\n",
    "            image = self.transforms(image)\n",
    "\n",
    "        return image, label\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data_info)\n",
    "    \n",
    "    def get_img_info(self, data_dir):\n",
    "        path_dir = os.path.join(data_dir, 'label.csv')\n",
    "        return pd.read_csv(path_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_folder = \"dataset03\"\n",
    "exp_name = f\"{dataset_folder}_ablation_study\"\n",
    "\n",
    "if not os.path.exists(f'./models/{exp_name}'):\n",
    "    os.mkdir(f'./models/{exp_name}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "kwargs = {'num_workers': 16}\n",
    "batch_size=128\n",
    "\n",
    "InDL = True\n",
    "if InDL:\n",
    "    transform = transforms.Compose(\n",
    "        [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) \n",
    "\n",
    "    trainset = MyDataset(f\"data/train/{dataset_folder}\", transform)\n",
    "    testset = MyDataset(f\"data/test/{dataset_folder}\", transform)\n",
    "else:\n",
    "    transform=transforms.Compose(\n",
    "        [transforms.ToTensor(),transforms.Normalize((0.1307,), (0.3081,))])\n",
    "\n",
    "    trainset = datasets.MNIST(root='data/mnist', train=True, download=True, transform=transform)\n",
    "    testset = datasets.MNIST(root='data/mnist', train=False, download=True, transform=transform)\n",
    "    \n",
    "    # trainset = datasets.CIFAR10(root='data/cifar10', train=True, download=True, transform=transform)\n",
    "    # testset = datasets.CIFAR10(root='data/cifar10', train=False, download=True, transform=transform)\n",
    "    \n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, **kwargs)\n",
    "test_loader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=True, **kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [07:22<00:00,  4.43s/it]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Xfc1wNYY5ADj9dDnv0MHasVBYJDl9ADUZ26xERGCYM2O1Mtesmb4EiNXK3B9/yPn27ea3qzBXt66cDh3yvV+rccwcAEyaBAwdCpx8srVjobBgZc5Bxsqcvx1biIiqNYa5qoxLk+zbV/X1UOPjjj0WaNFCLlutzP35p5wXFprfroJavXr6v4fVylxGBnDaaUBiorVjobBgmHOQqsyVlem7nxAR1TgMc1UZK3NA1aqbWZvVamVOhbkdO6repmlVK3NA1TBnNmaOHMMw56D0dH0WPlutRFRjcQJEVXbCnJ3K3OHDenv1wAE5GR08qC/YXK+e7zDnqzJHjmCYc5DLBWRny2WzD0hERDUCK3NVeYc576BmbLOqytyWLYF3zlB7pypFRZ5fq6pccrJUHFRY8zVmjmEuJjDMOUzNKDcu5E1EVKNEOszt2QMsXBhfg5NVmFNbahnDXEmJ/vo0awbk5ABJSbIWnK9JDYpqsSrelQRji9XlMq/MaZrvCRDkCIY5hzVrJucMc0RUY0U6zF1/vcyu/PHH8D1npKkwl5sr58Y2q/qDUbeuLBqcmKhXBgK1Wr3DnPckCOPkB8B8AsThw/puE6zMxQSGOYep/3+bNzt7HEREjol0mFu1Ss43bgzfc0aaCnOdOsm5MaSpMHfssfp1VidBBApzqjKnwpxZZU792yQkSJgkxzHMOYxhjohqvEhOgNA0zwH/8UItTWJWmVPj5VRrB7A+CWL9ejmvXVvOfYU5FeLMwpxxvJxa444cxTDnMIY5IqpWNA2YOBGYNcv6YyJZmSsu1qtcoYa5rVuBsWPDtweqP96Vud279eM3zmRV7FbmTjxRzr3HzHm3Wc0mQHC8XMxhmHMYx8wRUbWyYgXw0EPAbbdZf0wkw9y2bfrlUMPcLbfIFljPPBPa81ihwlzjxnqgUkHNLMxZqcyVl+u39+sn54Eqc2Zj5jiTNeYwzDlMVeZ27fJc8JuIKC6ppS5UKLDCLMyVlABud+jHY5zdWVIS/PNs26ZXGwsKQjsmK1SYS0/Xg5p3mDMbM+cvzG3ZIoEuJQXo2VOuCzQBwt+YOYa5mMEw5zA1GQlgdY6IqgEV4rzXSfPHLMxpWmjhSzGGuVAqc6++qq/htmZNaMcUSHm5/pqkp1cNamZj5tR9Nm/2HYJVi7VVK6BpU7nsa2kSf2GOlbmYwzDnMJeL4+aIqBrZs0fOy8oklFihgktqKpCWJqEOCE+rNRxh7uhRCXPK5s32wqpdxjaNMcxt2iQh16zN2rSpLFFSVuZ7z1UV5tq2lfYtAOzc6bnQsK8JEPv36yGRYS7mMMzFAPX/kWGOiOKesb1qddNpNZtVhbhwjpsLx5i5WbMkFDZqpFes1q0L/dh8MQbFtDTP8XD79+s/h7HNmpSkf+1rEoQKc23ayM/ickmQ+/tv/T6+2qyapn/f6jgB4qWXJDSnpQF9+gBLlvi//759wOjRsmBzairQvj0we3Y0jtQUw1wM4C4QRFRtqMocYD3MGdusQHjDXDgqc5Mny/n11+uzS9euDe24/DGOl3O5PNusqsVav77cbhRoEoQxzCUnAw0ayNfGVqt3Zc5YKVUhrrpV5mbMkFnKEyYAy5cD3boBAwZI1dJMWRlw1lnyOn/wgfwuvPqq3rp2AMNcDGCblYiqjWAqc77CnPfm7sEINcytXQvMmyehauRIoGNHuT6S4+ZUm7VWLTk3ToAwa7EqgSZBGMMcoLdajW1Z78ocUHXcXHWbADFpEnDDDcCIEbKu35QpEpRff938/q+/Lh9aPvlEZgW3bAmceqqEQIcwzMUAhjkiqjZirTIXapt1yhQ5P/dcCVUdOsjX0arMAXpIKyrS27vGFqvib605Tasa5nJy5NwY5rwnQABVw1x1qsyVlQHLlgH9++vXJSTI14sWmT9m1iwgL0/arNnZQOfOwOOPe449jLIkx74zVeKYOSKqNkKpzKWmynm4wpzb7dlCtDs79tAhYOpUuTxqlJxHozLnHebq1QMyMuT4f/hBrjOrzPlrs+7aJWHW5ZLZrEDVylxZmf69jUEtDsNcSUkJiouLK79OTU1Fqvr9Mtq9W0JYdrbn9dnZvv+NN2wAvvkGGDpUxsmtXw/cdJNMlJkwIYw/hXWszMUA45g5TXP2WIiIQhJMZS5SEyB27/acUWu3MjdjhgSXli1lDBWgV+bWrYvcG7Z3mHO59KC2cKGc+2uzmlXmVFXu2GP10KzCnAq8xra2cXKD979HHEyAyM3NRVZWVuUpPz8/fE/udssEkldekfX6hgwB7r9fr+I6gJW5GKCq5YcPy6QiNSaViCjuxFKbVbVYXS7P2ZhWqYkPN94oy34AQOvWMnP04EF5frN2Z6i8wxwgQe233/TgZfZ9jWPrNM1z31S1J6tqsQJV26yqqpqVpf+8QFyOmSsoKEBTw4QE06ocIH9wExP1xa6VoiI97HrLyZEJJMbXqFMneR3LyvTf4yhiZS4GpKbqvzNstRJRXAvnBIhQw5ya/KAqVkeP6t8rkMJCYOlSGT917bX69cnJeiCKVKvVLMypoKaYVeaaNZMAd/iwtFWNvMfLAVXbrL7ap3HYZs3IyEBmZmblyWeYU7thzJunX+d2y9d5eeaP6ddPwrFxceZ16yTkORDkAIa5mMFxc0QU9w4fBo4c0b+2urCud5gz29w9GCrMtW+vX2e1OqfC0DHHAA0bet4W6UkQvipzRmZhLiUFaNJELnuPm/MX5lS1z2zyA+AZ5kpL9dm2MRzmbBk7VpYWmTYNWL1axkcePCizWwFg2DBg3Dj9/qNGSQX61lslxH3+uUyAGD3ameMHw1zM4FpzRBT3vPdjjZXKXIsWsl4aYD3MqYV0jzmm6m2RngShwpK/ypyvNc18LU9ipTLnvcacYgxzxn+TjAzzY4g3Q4YATz8NjB8PdO8OrFgBzJmjT4rYvNlzIk2zZsCXX0rltmtX4JZbJNjde68TRw+AY+ZiBpcnIaK4ZxwvB9ifABHu2axqzFyTJrIJ9pEj9sNc/fpVb4tWZU6tMwd4VuYaNPC8zahlS5nxumGD5/VmYU6NmSsulu9ptsYc4Pnvoe6Tmek5ZizejRkjJzPz51e9Li8P+OmniB6SHazMxQiGOSKKe7FamVNhDrC+PIkKpk5U5gK1Wc1arMrxx8v5v/+tB9eSEn03A2OYy8zUK5aFhdbarHEw+aEmYpiLEQxzRBT3gq3MRTrMNW2qh7lwVua2bLH+M9phFuaM1Th/Ye7GG2UduS1bgIcekutUla5+fc8Q5nJ5tlqtTICIg8kPNRHDXIxQ/zc5Zo6I4lasVea826yA9TDnrzJ3zDH6GlJqR4ZwMgtzxj1a/S2Hkp4OvPiiXH72WeDXX81brIpxeRIrlTmGuZjEMBcjVGVu+3aZPU9EFHfCXZkrKfFc/sGOo0f11mIwYc5fZQ6I7Lg5szAH6JMg/FXmAOCcc4BLLpGdDW680XyNOcVYmfMV5ox75cbBgsE1EcNcjGjYUMb+aprnVoJERHFDhQE1DivUCRCaZn8LLkXN0ExOlipasGHOrDIHRHbcnK8wN3IkcMIJwEUXBX6O556Tn3nRIrkMAG3bVr2fcXmSQG1W4wQIVuZiCsNcjEhI4FpzRBTnVGVOvZlZCXMVFfoG5aoyl5amXw621ao+FefkyBusWkYjHG1WILKVObOlSQBg8GBgyRLPdfN8adoUeOQRuayW1Qi1zVpero9DZJiLKQxzMYRhjojimgpAakyXlUWDjeNKjKvnhzpuzjiTFQh/mzUalTlfy49YNWaMrJumWG2zege12rX1ZUjU+nUMczGF68zFkFAWDi4vBzZulCq6cTu+cDp0CPj5Z1nCaPly+fDWrp18SGzXTj7glZbK/dRC8HXrSkh1aIcTIoomFQZUmLNSmTNur+Ud5nbtCn+YC8fSJIBemVu3Tsb1JYSxNuKrzWpXUpJs/q62pTKr6Jm1Wb0rcy6XvJn//bfs+wpwzFyMYZiLIcEuT6JpwGWXAR9/DPTuDTzwAPCPf4Qn1GmaLFc0bRrwyy8SGu1yuSTotWghi2U//DDQqFHoxxYu5eXyHrZ3r7x/p6YC3bpFLhQTVVvBtFn9hTkg9DCndkqwU5nTtMCVuVatZDzeoUPA1q36G3g4hCvMAUCfPsBHH8mna7WjgZEKc9u3+w5zgPx7GMMcK3MxhWEuhgQb5t55R4IcIMMpzj9fwsj99wPnnitdjNJSec88fBjYvVsmee3aJedt2gCXXlr1g2VZmUyEeuMN/bomTYC+fWUM7oEDwB9/yGndOvnA63JJZyA9XULR33/Le8j27XJatEiOcf58Wa/SSTt2AKecok/0Mjr/fOCVV8zf+4jIh1Aqc0lJnm9CoYY547IkgL0wd+CA3v71VZlLTpY3zzVrZNxcrIY5ALjwQt+3qTFzxu2qzIKauk4Fdoa5mMIwF0OCGTNXWCjbwgHAXXfJe+FLLwErV0q1zqpJk6QC17OnfL13L3DxxcC338pz5ufL9nXNm5tXrDRN3pNTUjxv1zQJjZs2yVJHt9wiFb7Bg4HZs/XJa0544w3PIJeRIR/Ct28HZs2SdvKUKTLDn4gsCKYyp2ayeo/FcHLMnPo5UlP9B6qOHSXMrVkDnHVWcMdpJtxhzh/vNklamj4b2cjXDFeKCZwAEUPsjpnTNGD0aAlePXoAjz0GPPGEBKcJE6r+X0tOlvezVq2kHfuPfwBXXSUhZskSqbaNHi3j4fr2lSBXpw7w2WfA3XdLm9RX69Hlkvc979tdLnmvOOEE4PLLgS++kOf85hv53moSW7RpmlQ0AQm/R4/K9oR//SXjArt2laripZcCQ4dWXT6LiLy43XqbzjgBQtP8P857jTnFyTarscXqb7xFpGa0RjPMpaR4Vh99hTSGuZjGMBdD1IfZ/fs937/++ksf8mA0c6Zcn5QkVabkZLm+fn1g4kSpiBUXy+PcbnnPLCmRnV0WLwb++1/grbfkfejKK/XxcT17ygfNY48FFi4EBg0K38/Ys6e0hJOTgQ8+kEqdeq/fu1f2Lf7iC31mfqSsWgUUFMj72JVXymuodO0KLF0qbeqEBODdd4FTT+VizkR+FRfrC/yqNzNNC/yfOVJhzrvNamdpkkCTH5RIzWiNZpgD9FYrYD5eDqg64YETIGIKw1wMqVNHH2urqnOrVgG9eknLs2VL4PHHJfTs2iVVNAC47z4ZI+ctKUnev8wqZkY5OVKl+vZbIDdXrjv+eAl8Zs8bqv79gbfflmP697/le2Vny8+elyeLl7dvD0ydGrnK3bvvyvm555p/wExJAR59FPjxR1lv9LffpOUabZoGrFghfyeJYpoaL1erlmcgCNRqjUSYO3hQf1wwbdZAkx+USFTmysv1T46hLk1ilZoEAfgOc6zMxTSGuRhjHDe3Zo0En7//lmBWVCTVombNgDPOkIkMXbrIdeFw2mkSHL77TsaLqffASLjsMuD55+XyihWeu+5kZ8vksBEjpH08Z07gTo0dbjfw3nty+cor/d+3Tx8JdYBUO723noykpUvl37lHD+DEE60vj0XkCFXNql9fStpWd4FQYc57AG0oYU4N5q9dW59pZWdpEquVORXmtm4N339QYyUzWpU5Y5iz2mZlZS6mMMzFGDVu7ptv5A/5zp3yx3z7dqlmde0q742//SZrOL7xRnjXcEtOBk4+2Xz8a7iNGSOTIN55R8apFRdLZ+Svv4B//UveO1atkjbvSScBr77qO0ypte2s+PFHCcsZGVKZC+S664DjjpP3dxXsIunPP2V8Ye/eMusXAFavBkaNCm+oJQorFYBUZad2bTkPtHBwJCZAGFusqi0Ricpc/fr686rtw0JlfL2i8UYMWGuzGsNc7dr6uB6KCQxzMUaFuWeekQ+XXboAc+fK3q1Dh0oVa84cqWy9/LI++zReDRok1bGePfUhLWlpwJ13Sqi54w55j//xR9mWsHFjmQn71lsyA/eqqyRoqZmoN9+sL1Dui2qxXnSRtS5GUpL8ewDACy+YL2Wyf3/ordDiYvl5O3UCZsyQv0HDhkkVMTFRwrxxmRjl8GFpv6tqI5Ej1CctFYBUmHOizeo9kxUILswFqswBMg4DkFZJOBjHy0VrsUu7bVZW5WKO42HupZdkLFhamrS0lizxf/99+2SsWE6OVOXbt5fqTijPGUuMSxV16gR8/bXn+4nLBQwYIH/sr7su+scXTfXrA08/LRM2nnxSqpJlZcAnn0jIueMOqeoVFEjr9MgR4MUXZReMYcOA33+v+pxHjwLvvy+XA7VYjQYMkOB59Chwzz369ZoGvP66tL6bNJElXLwnqgDy9+j554Hx44FlyzwrbJomQaxjRwmoR48CAwfKEi7TpkmVTm2xOHq0VCuVNWukgnf//fLz3HJLcAs7E4XMV2XOyTCnZrICepgrK/NcqNiM1TYrENkwFy1W2qzGAMfxcjHH0TA3YwYwdqwso7F8uQy2HzBAHz/lraxMlvL56y+ZCbl2rbTejP9f7T5nrFETDtq1A+bNi62dEpzStKksjbJyJfDrrxKmevWSCt0jj8jSKdu2SWv6rLNk0sRbbwGdO1cNN3PnyofuRo2kjW3H009Lheyjj2RcYWGhLC583XUyDOfgQZmMkpsrM3Y1TSay3HWXhL1bb5Xj7dVLPoQ88ADw1VfAmWdKENuxQ/7d58yRGb3GySf33CO/x0eOSFX2wAHgzTelovnbb/rfzxdeAM47L/gJgERBi5fKnJVjstpmBapfmLNSmWOYiz2ag3r31rTRo/WvKyo0rUkTTcvPN7//5Mma1rq1ppWVhe85zWzZskUDoG3ZssX6g8LE7da0+fM1bd++qH/ramPpUk27+GJNc7k0DdC0f/xD0w4ckNuGDpXrbr45uOceNUoe37atph1zjFxOSdG0J5/UtLfekt81iXGa1rmzpiUl6V936iTHVauWfp06paVp2iOPaNqRI76/986d+vO3aqU/9owzNG3HDk378EP9uY87TtM2bAjuZyQKyp13yi/f2LHy9amnytczZvh/3Hvv6b/IRmvWyPWZmfaPZcgQeeykSZ7Xp6bK9Zs3+398Xp7c76OPAn8v9aby9NP2j9PM/PnyfB07huf5rCgo0N9QXnvN/D4rVuj3Oeec6B2bTU7+/XaSY5W5sjJpN/Xvr1+XkCBfL1pk/phZs2TpitGjZcZj584yVkgtXxHMc8Yal0vWNOOQhOD16iWV2w8+kFb7Z5/JTN0NG6RFC9hrsRpNnCiT49avlw/v3bvL5I2775bxe2vXSsUtNVUqZuXl8r0/+0y+/uADqRK/+65U9erVAy64QFrF6nG+NGwITJ8u1cGNG+V3+6GHpLrXuLGMAfz+eylG/P67DDH45Zfgfk4i24KtzAWaAFFSoq9fZ5VZmxWwPm7OycqcmsnFyhzZ4FiY271bQpj33pfZ2b4nBW3YIH8MKypknNyDD8rAdDXDMJjnBIDS0lIUFxdXnkqsTF2nmHfRRdJ6bdBA39Xh4EHZAaNPn+Ces1Ej4P/+T84feEDW4uvSRb+9Th1ppa5ZIx80li6V9fvOPVffdrJOHeCKK4BPP5WhOZ98Isdkxckny9CCU06RNvz48RLulJ49ZYxojx6yFuGgQRL8oq24WF5zqkGMS5MA4Wuzapq15USMzNqsgPXlSWJhAkS01pgDJJypT5JWFg1mtSHmOD4Bwg63W/6IvvKK/NEaMkQGfoe6mGt+fj6ysrIqT7lq5VyKe3l5UpVt107/m3LllaFNErvmGlnz75FHfC8L07IlMG6cVAnDbcQIYMECqfiZadpUljTp1k2Oc+DA8P2dseq662QLt48/ju73JQepyly4JkCkpelbs9gJc5pWdfcHxUplzu2uWmX0pzqMmXO5ZLCvy+X7k2Vmpv7GycpczHEszDVoIBWFoiLP64uKPCu+Rjk5MnDcWIno1EmqbmVlwT0nAIwbNw779++vPBUUFAT3Q1FMattWljY55RRZwmTECKePKPIyM6V63bw5sG6dtHQDLfdVUaEvfxLKenYlJTIkApCZ5VRDeFfmVBgJNsy5XPpz2Nnfb98+fUq5cf00wFqY279fb+taqcyp+6hqXqicCHOAjAVZulQ24TaTkKAvwMwwF3McC3MpKVJdmzdPv87tlq/z8swf06+fjFUyDp9Yt07+v6akBPecAJCamorMzMzKU4Za8IyqjQYNpFq1axfQpo3TRxMdTZrIzNh69aQ6ecUVvpct+f57qSJefTVw7bUy6zdYc+bof5/nzZPhEVQDhLo0idmAUdVqDPRJxEi1WOvXr9qqtBLm1M9Ru7b/QaxKdajMAfKGEWjhUhXiGOZijqNt1rFjZfzPtGn6CvcHD+qVk2HDpFWljBol/89uvVVC3Oefy7gktUepleekmsvlsvbeXJ106gT897/SsZo1SyZbPP+8rF+4fbvsQnTllVK1XLFCf9wjjwRfnfv0U8+vzRY6pmrI1wSIYHeAAPQwZqcy52u8HGAtzNmZ/ABUnzBnBcNczEpy8psPGSKVkvHjpVXavbt8qlcTGDZv1geNA7JW15dfArffLoPZmzaVYGdcxDXQcxLVNP36yezZiy+W1qv3ItuABN0bbpAPRr17AwsX+h+X58vRo/IhC5Dt2l58EZg6VWYBG4dHUDVTVqZX4MI1Zg4Irs3qb8FfO5U5Ky1W4/327JFWUEKINZJYDnP/+IeE5WBnkFHEOBrmAHnDHzPG/Da1L6VRXh7w00/BPydRTTR4sLRSZ8+WinVBgQxZqKiQsPf888Dxx8t9r7sO+Pe/ZZa43TC3cKEMWWrQQHbtePddqf599ZXMrKVqSlXlXC59pmM4wlwwbVYV1MyGy6jrwlmZU2GuokLG2/maDWqVE0uTWPXoo1K2j9Y2Y2RZXM1mJaLg9esHPPaY7GCxZo38fdy2TUKeCnKArJmXlCTj3bzXZywvlzF1nTvrEwaNVIv1H/+Qv0VXXy1f/+c/kfmZKEaoalbdunoJNpxhzk5lTgU1444PipWlSewsSwLI2A0VEsPRanViaRI7GORiEsMcUQ2VkiLDirzfm1u0AIYPl8tqT1hACg/Dh8sYuN9/l6EMRpqmh7kLLpBztX/wrFnxs6UeBcF7WRIgPBMggmmzqjCnvr9RJNqsQHjHzcVym5ViFsMcEVUxbpwUWL74QnZVcbuBf/5T2qaq8DJ1qoQ6ZdUq2Tc5LU32yAVkQeUTTpCK3ltvRfunoKjxXpYEcK4yp76fv8pcONusQHiXJ2GYoyAwzBFRFW3a6FuePfoocMstwGuvydju996T3TXcbs/Z5qoqd9ZZnkWR66+X89deC239OophoVTmrMxmDWbMXLBhjpU5ikMMc0Rk6r77pAX7ySey+K/LJdW4Sy+VJYESE2XZk++/l/t7t1iVyy+Xv0urV8fPHslkk1llLtRFg4HQxswF22YNpjLHMEcOY5gjIlMdO0pwU6ZM0Sc0dOigV9zuuQfYskXasS6XTH4wyszUn+e11yJ/3OSAcIyZC9fSJOFqs7IyV7O89JLsw5iWJkuvLFni+75Tp8qbnfGUlhatIzXFMEdEPuXnAyedJAtxjxzpeduECfL3ZtEi/bYTTzRf01EFv3ffBXbsiOwxkwP8jZkL1CIN9w4Q/tqsVpYmMftZAuGYufg2Y4bsODBhArB8uWxsPWCA/1lbmZnyZqZOmzZF73hNMMwRkU+tW0sbVYUxo5wcef8DZGFuoGqLVenXT4LekSOy/lw4VFTIhIuvvwbefFMWCyeHeG/lBehhrqzM9z5y6nYgfpcmAcJbmYvldeaqq0mTZNX0ESOA3FxpQ6SnA6+/7vsxLpds+q5ODu9MwDBHREG76y797xjgO8y5XMDDD8vlKVPM16iz6rHHZJuy9HSgVSuZcDF8uN4CJgd4b+UFeI5Z89dq9TcBIpQ2azBj5srLZeFfwPk2a6yuMxcnSkpKUFxcXHkqVb9n3srKZIxI//76dQkJ8rW/Qb4HDsg6Ts2ayRufcWq/AxjmiChomZnAgw/K5Y4d5eRL//7Ssi0tlfZtMFauBB54QBY9LisDkpNl/F5Cgmz19+uvwT0vhcisNZmSoq9j4y/MOVGZ8xXmVCgF7O3kwDFzMSc3NxdZWVmVp3xfbzq7d0uZ37uylp0te4Ka6dBBqnaffgq8/bZM7e/bV7a7cQjDHBGFZPRoqbZNn+7/fsbq3KuvyqQJux5/XM4vuADYsEH+xq9ZI/vOAtItIQeYTYBwuaxNgojUdl7+wlxpqWwk7E21WLOyZBsUqzhmLuYUFBRg//79ladxxnWUQpWXBwwbJpu/n3qqbKvTsCHw8svh+x42McwRUUgSE2VB4W7dAt/39NNlv9eyMj2YWbVmDTBzplx+5BFpsarCzx13yPm778o+4BRlviYNhCvMhXtpEl/HFMzkB0CvzP39t1RpvO3bB/z5Z+DnOXpUD5kMcyHJyMhAZmZm5SnVbIINIP92iYlAUZHn9UVFMhbOiuRkoEcP2fDaIQxzRBRVDz0k56+9JhMYlF27gKeflskMZvLzZdHhCy6QnSWM+vSRFu7Ro8ALL0TksMkXTTOvzAH2wlw4tvPSNP9Lk6SkyB9ewLzVGszkB+P93W4Jbt7OOUdac4HacMafk2EuOlJSgJ49ZTNqxe2Wr/PyrD1HRYVsgZOTE5ljtIBhjoii6pRTgDPPlOD12GPyHnj99TKO+K67ZDLDK694PmbjRuCdd+Ty/febP6+qzk2ZUvXvdGkp8OKLMuaOwuzAAX22qndFy8rCweHcAcI4c9YszAH+lycJtjKXkqI/r3ertbwcWLpU/uBv2OD/edTP6XKZh1uKjLFjZezHtGmyuvmoUfI7O2KE3D5smOd2Nw8/DHz1lfx7Ll8OXHWVLE1iNu0/ShjmiCjqjNW5rl3lvLRUWqcAcNNN+nIngCxnUlEBnH227PVq5rzzgHbtpDDyxhv69QcOAOeeC9x8M2e8RoSqyqWkVJ2BGe02qzGgmbVZAf/LkwRbmQN8T4LYvFkPmMXF/p/DuCyJy2X/GCg4Q4ZIW2D8eBkHt2KFvAGpSRGbN3sukLl3ryxl0qmTVF2Li4Eff5RlTRzCMEdEUdevHzBwoHTFEhJkh4gffpBhRcOHS3C79FKppG3bpoezBx7w/ZyJicDtt8vlZ5+V59izR2bRqg7KqlXBTbwgP4zVLO8AYmXh4HDuAKFCY2qq7wkM/ma0BrMvq+IrzBnHygUKc1yWxDljxkh1rbQUWLxYxm4o8+fLrg/Ks8/q9y0sBD7/XMbMOYhhjogc8fbbMvlrwwbg/fdlZr/LJS3W00/XK2p33y1/708+WU7+DB8uf4c3bpTdeU45Rd6X69cH2rSR+3z5ZeR/thrFX2sy2rNZ/c1kVfyFuWD2ZVXCGeY4Xo5sYpgjIkccc4xsA9aihef1KSnAhx/KmnXbtskMVcB/VU5JT5fhLgBw662yjmeTJsB338mwF8CzfUth4GvyAxD6BIhg26yhhrlgKnO+licxhjl/O08ADHMUNIY5Ioo59eoBs2cDjRrJ1716yU4PVowZoxd52rQBFi4EjjtO2roAMHeu+RJjFKRQKnOaFt4xc/52f1CstFlZmaM4wzBHRDGpVSupog0eLDNUrY4Hz84G/u//ZMzd99/rkyp69pTiSXGxtF4pTEKpzBlTtb8xc+Xl1hK4k5U5hjlyEMMcEcWsHj1kcfWePe097sYbZRyecdmnxESZDQuw1RpWoVTmVFUO8F+ZA6xV50INc+GuzGkawxxFBcMcEdUYqtVqNgniwAHZFuyRR6J7THEvlMpcoDCXlqZfthPm/LVZ1Xpw4V6axGzM3M6dnj97oDFzxqVJiGxgmCOiGkNV5n7+Wf7OGj37rFQBJ0yQVQfIIn/VrECLBqswl5BgvpSIy2Vv3Jy/3R8UX5W50lL98eFqs3pv4cXKHEUIwxwR1RiNG8uaoIBMhFB27QL+9S+5rGmyGDxZpMJcKJU5s6qcYmd5klDarOrnSEgAsrICfy9v4QxzXGeObGKYI6IaRbVajePmHn9cOmCZmfL1f/7DGa+Wqf1GmzSpelugMOdvKy/FTmXOSpvVV5hT7dF69STQ2aXC3J49srcnoIc5tcghlyahCGGYI6IaxThuzu2Wluq//y3XvfuuVO+KioBPPnHsEOOHpslWR0DVBQOBwDtA2KnMRbrNGsrkB+Pj3G7ZUw7Qw5zaHYBtVooQhjkiqlHy8mQM/K5dwC+/yBi5sjLZdeKcc/S9sqdMcfY4o+7QIfmh7ex3VlQEHDkilaxjj616ezjarHa29AqlzRrK5AdAfgZV2lWtVhXmVG+fYY4ihGGOiGqUlBTgzDPl8jPPAG++KZefeELG299wg2STb74B1q517jij7v33ZfsMtcGtFWqmSJMm5oHMapgz2/1BidaYuVDDHFB13Nz69XKuKnMlJVLN9IVhjoLEMEdENY5qtb73nvxtvfhioHdvua55c6nQAbJPbI2xa5ecf/ONPuYrkL/+kvOWLc1vD+cECDtt1mCWJgm1zQp4Lk9SUqK/pirMaZr/rc24NAkFiWGOiGqcAQP0y4mJwGOPed5+441y/sYb1neSinuqKrR3L7BqlbXHqMpcsGHOygSIeGmzAp6VOdVibdBABmKqSRX+Wq2szFGQGOaIqMZp2RLo0EEuX3utflkZOFDG8+/dC8ycGfXDc4axjblggbXHqMqc2eQHQA8lhw6ZtxdjcWmSUCpzZmGuTRvp36vxdFbCHJcmIZsY5oioRpo0CbjqqqpVOUCqdSNHyuUaMxEimDBntTKnaeaVtXC3We0sTXLkiOz5qqxbJ+dNmwb+Pr74CnOAHub8LU/CyhwFiWGOiGqkc84B3noLaNjQ/PZrr5VNCRYtkkkSxp2nqiVjmPvuO/8D9RWrlTnAvNVqZwJEuJcmMd7/yBFg8WK53K9f4O/ji3HMnHeYU2P12GalCGCYIyIy0bgxcOmlcnn4cFl54667gDVrnD2uiDGGud27gYIC//fXtMATIBIT9f1V/YW5aI6ZS0nRtw5T9//pJxm/17gx0L594O/ji5XKHMMcRQDDHBGRDy+/DNxzj/yN37ULePppoFMn4PLLrU/4jBveY9ICtVr//lt/TLNmvu/nb+FgOztABBozp2nW2qwuV9Vxc+pnPfVUuT1YDHPkEIY5IiIfMjJk/bnNm4FPPwXOP1+KTTNmyKlaUZUvNRtk/nz/91dVuZwcvfpmxt+M1nCOmSst1RO2v8ocUHV5EmOYC4UKczt26DtjcMwcRQHDHBFRAMnJEuQ+/RSYOFGuu//+ajaOTgWJQYPkfMEC/+Pm1OQHX+PllFDDnNU2q3F2qr/KHOBZmSstlYGRQOhhTo2Z27BBgmV6upR1AWtj5rjOHAWJYY6IyIbbb5e/zxs3Shu22lBh7rTTpNK2c6f/LTACjZdTrIS5cOwAocJcrVpSPvXHGOaWLpUJEA0bSg89FKoyp7RurbdtA7VZjx6VE8AwR7YxzBER2VC7tl6de/jhwNttxg0VlurWBU48US77GzcXaFkSJVptViu7PyjGMKd+xlNOCW28HFB1wWHVYgUChznjz8d15sgmhjkiIpuuvVYmPe7eLZMiqgXjeC3VbvQX5gItS6KoKpNZmLMzAcJqmzXQeDnjfYxh7rTTAj8ukORkICtL/9oszPkaM6def5fLf6WSyATDHBGRTcnJQH6+XH7mGRnvHvd8hTlf4+aiVZkz7iLhTzBhbu9e4Icf5HKo4+UUY3XOGOYCjZkzvv6hVgipxmGYIyIKwuDB0o08dEjarXYdPqxvBxoTjGHixBMlYG3fri+xYWRcYy7SEyDsVubstFnnz5efu3594LjjAj/OCuO4OTttVs5kpRAkOX0ARETxyOUCnnxSCjqvvgrcdlvVPV7NbNwIvPQS8Nprkk8++UT2gnWUcbut9HQJUH36AN9/L4GnbVvP++/bp4eScIS5cOwAYWX3B0VVyb75Rs5POQVICFNtI1CYC9RmZZirvn791fp9u3a19dQMc0REQTrlFOAf/wA++wwYO1bOfXXIvv0WeO454L//9excXnyxZIo+faJyyOaOHNEvqzBx6qkS5hYsAK6/3vP+qsXasGHg8BHtpUnstFnVzx2uFiugt1kTEz2DrtUJEAxz1Vf37vIG4WvogrrN5QIqKmw9NdusREQhePppySKzZwMff2x+nxdfBM44A5g1S96rzz5bLg8YIAWZc891eJsw43g0VQnzN27O6rIkgP8dIOy0Wa2OmbPTZlXCGeZUZa5FCxlcqdgZM0fV08aNsgbhxo3mJ3Xbhg22n5qVOSKiEHToIFt+PfIIcMstwFln6X+3AeDnn6VqBwDXXCP37dhRvj79dODMM4ElSyTY/fCD7AEbdSpIGPctzcuTy1u2yB+Y1q31+1tdMBjwX5kL52xWO21W433q1rXd0vJLhTljixWwPmaOy5JUX1b+vwSJYY6IKETjxgHvvCMfqCdMACZNkuv37weGDJG1YAcPBl5/3bMNW6cO8PnnwEknyfq8AwcC330n4/GD5XYDixcDjRpVzRM+mQWJ2rWBvn3lgD7/HLj5Zv22YCpz4WizqhaUmWDarABw8smBFxm244wzZHmSiy7yvF6FucOHgfJyPTQrrMxVf7NmWb/v+efbeuqgwtyWLfL/SX2CXLIEePddIDcXGDkymGckIopftWrJpIZBg4D/+z9g2DCgWzd5P9ywQT6Qv/aaeQ5p0AD48kvJTb//Dvzzn8DMmfaPobAQmDpVJmNs2ABkZwNbt1bNDKZ8BYnzz5cw9+mnnmHO6rIkQPgmQAAyxs1X5SrYMBfOFisgM4H37Kk6ocJYri0pAerV87ydYa76u/BCa/eL1pi5K6+UwbyAvIGcdZYEuvvvD26KPhFRvBs4ELjsMqmM3XgjMGUK8P77EqamT6/6t9uoRQvJS4Cc79tn/ftu2wZccgnQrJlUCNVwm6Ii6Y5a4i/MATJuznhQVpclMT5nqEuTAP5brcGOmQt3mAPMZ8ampOih1azVyjDnrJdekg8naWkyG2nJEmuPmz5dwpeVoOZ2WzvZDHJAkGHut9+A3r3l8vvvA507Az/+KG2GqVODeUYiovj37LNSgFm8GLjpJrkuP1/fHcufXr2ku3H0qHQ1rbr+euDDD6Vzl5cHvPGGvCcDNiZV+AoS7drJfqXl5TLDQ4lmmzU5WW+D+gtzdsbMqV0aMjJkhmG0+FuehGHOOTNmyMDWCROA5culrD5ggOxP7M9ffwF33imteocFFeaOHtU/YHz9tf7hrWPHarISOhFREJo0AR57TP/6nHP0yQ9WqGFWH31k7f6bNkmLFpDJEz/+KJMsunSR6yyHOX/LYlxwgZyr0mFxseycAERnAoTxuKxU5qyEuZ49JW2/9JLFPnSY+JsEwaVJnDNpEnDDDcCIEfKJasoU+Xd4/XXfj6moAIYOBR56yHNykB0HD8qHpClTgOef9zzZFNRv8XHHyfc+91xg7lyZxQXIYuHe+wwTEdUkN90EfPGFvB9Om2ZvLdqLLgIefVQef+hQ4L/rU6fKnIDTT5cxd4qaLbt6tcVv7K8qdMEFwBNPyEGVlenj5erX9xwH5kuolTlAWq0lJf6XJ7HTZk1MlCAXbf6WJ2FlzhllZcCyZTJGQUlIAPr3BxYt8v24hx+WWUbXXSfrMdr1yy/yae/QIfm/Ub++bPacni7Pe8sttp4uqMrck08CL78s+xJfcYVUJAGZqKHar0RENVFionzYXrHCczMAK7p3l87l4cN6xc2Xigq9cHDddZ63qTAXcpsVkDf1xo0lTM2fb29ZEiD0CRCAteVJ7LRZneKvMscwF1YlJSUoLi6uPJWqKrC33bvlP1N2tuf12dkyKcDMwoUyo+nVV4M/wNtvB847T6rctWoBP/0k/7d69pTFK20KKsyddpr8/Lt3e1YhR46Uih0REdnncllvtc6bB2zeLMukea+C0amTnK9Z43uxeQ/+gkRCgvzRAaTVame8HBD6osHG4wpXm9UpVsbMcZ25sMjNzUVWVlblKT8/PzxPXFICXH21BDm7n9aMVqwA7rhD/n8lJsqQg2bNgKeeAu67z/bTBdVmVcv9qNlZmzbJyuedOsmYQSIiCs5FF8kQnv/+V7KOr5zz2mtyPnRo1b//7drJ34i9e2UMt3fRoYpAQeKCC+SP16xZ+n3sVubKyqqur2anzWo8TjN22qxOYWUuagoKCtC0adPKr1N9VX8bNJAwVVTkeX1RkVSkvf35p3ygUR9wAJmBCsjv9tq11hZ4TE7Wx2A0aiSfzDp1ksk5W7YEfryXoCpzF1wAvPmmXN63T2bxPvOMzMydPDmYZyQiIkBmpDZuLAsOqyWgvO3erW8d5r1tKiCrK7RqJZcttVoDBYkzz5SQtHUr8Mkncp3dyhxQtdVqdQJEdWmzcsxc1GRkZCAzM7Py5DPMpaRIa3PePP06t1u+zsurev+OHYFVq6Sypk7nny8DV1eskOqaFT16AEuXyuVTTwXGj5clQW67TZ+ObkNQYW75cn0m7gcfyKe+TZsk4AUxCYOIiP4nIUFfsurDD83v8/bbsqrA8cf7XlnD1iSIQEEiLU1vu/z5p5xbDXMpKfrSIt5hzm5lzleY07T4arMyzMWWsWOl8jxtmvyHGTVKfldHjJDbhw3TJ0ikpUnYMp7q1pWg3rlz4N9l5fHHgZwcufzYY9LqHDUK2LVLJiXYFFSYO3RI/4Dx1VfSFkhIkLWU1NhYIiIKjhoD98knVdcP1TTgP/+Ry94TH4xsTYKwsiyGWqJEsdpmdbl8LxwcrjFzauwPEB9hzmzM3N9/y3ndulE7HPqfIUNk0sH48fLpaMUKYM4cfXzC5s3hX3etVy+p5gHSZp0zR0L+smVBrX0YVJhr21beZLZskRlXZ58t1+/cqf+u2mFn4eWpU+W9wXhKS/O8zzXXVL3PwIH2j4uIyAmnnSYf1HftkvXjjJYskW2/0tJkNx5fjJMgArJSFTr3XM89TK1W5gDfM1rtzmb1NWZOVeWA2K5s+WuzqnFSVtt0FF5jxkg1qrRUVv3u00e/bf58/zsiTJ2qDz+wauNG4I8/ql7/xx/6JCMbggpz48fLosctW8qsddVW/uoraQPbEczCy5mZEpLVyawaOHCg533ee8/ecREROSU5WV+M3XtWq6rKXXKJ/yJOWNusgCwietJJcjkz014FKVCYC7XNqp43Pd3ewn7R5qvNWlIigyQBfdNzqt6uuUZW+fa2eLHcZlNQv/WXXCJVx59/9lwL6cwzZTsbO4JZeNnlkgHC6mQ2Uys11fM+/vZFJCKKNcYlShYvlmE0N94IvPuuXG828cFIhbnNm82XePNgdbyWarXaqcoB5mGuvFyfBRhqmzUexssBvtusqiqXlWVtIWaKf7/8AvTrV/X6E0+UNq9NQe9jokLS1q3y9bHH2l8wONiFlw8ckOEabrcMAH78cdmVwmj+fGlD16sHnHGGrKrua3eK0tJSjwUFS8zGMxARRdFZZ0kG2rKl6t6uxx0HnHKK/8cfcwzQsKG0atetC9A1sRrmrr1WkuWllwY8fg9mYU5V5YDQlyaJh2VJAN+VOfWHlC3WmsPlMh87uX9/1YGyFgRVmXO7ZSeLrCwJVS1aSMX9kUf0D1pWBLPwcocOUrX79FOZ0eV2yzY26v8CIC3WN9+UmcVPPgksWAAMGuT79cnPz/dYXDA3N9f6D0FEFAG1aumT6Ro1kuEn99wDTJ8u72kuV+DnsNxqtbpgbVaWHMDFFwf+5kZmCwcHE+YCtVljvTLna8wcx8vVPKecAuTnewaTigq5Tg1nsCGoytz998uClU88oVcJFy4EJk4Ejhzx3Gg63PLyPJd+6dtXBvq+/LK+R+zll+u3d+kCdO0qa/jNny+tYG/jxo3DWMNu2Nu2bWOgIyLHPf+8vJ9mZFgLb946dpRtIwNOgoj0shiBKnPJyf4fHyjMxVub1VeY43i5muPJJyXQdeigr/X2/ffyu/HNN7afLqgwN22aDMJVA3QBCUxNm8om01bDnN2Fl80kJ0v7YP163/dp3Vq+1/r15mEuNTXVY0HBYrOZRkREUeZyBbdCgGJ5RquVpUlCoSpSapA/4Dn5IVBStTpmLl7arCUlspSK+rlZmat5cnOBX38FXnwRWLlSPrAMGyazauvXt/10QYW5PXv08r1Rx45ym1XGhZfVIplq4eUxY6w9R0WFLMZ8zjm+77N1qyzho9bnIyKqCWy3WSMV5po0kfNt2/TrrM5kBQKPmYuXNqsKc0ePyhIYal0tjpmrmZo0kUH/YRDUmLlu3SRMenvxRanQ2WFn4WVAxup99RWwYYMsZXLVVbI0iZrZdeAAcNddwE8/yVIt8+bJBKy2bblvLBHVLCrMrVsXYEx1pMOcah8a95y0upUXUH3arMbjM3aA2Gatmb7/XkJM3776B5233pJxazYFFeaeekomIeTmygrk110nl6dOlUWU7bC78PLevbKUSadOUo0rLpalWtQQt8REqVyefz7Qvr0cW8+e8poFWpeSiCimTJ0qg4R9zQgLoEULKf50L1uMo8f39v1HItJhTlWcjDPV7FTmqsvSJAkJ+jGqmYyaxjZrTfThh1JhqlVLKlPqw83+/UFV64IKc6eeKp/0Bg8G9u2T00UXyarkb71l//nsLLz87LP6fQsLgc8/95xyX6uWrH23c6e8V/z1F/DKK+Zr0RERxbTXXpM2w2efBfXwhAQZX30JPkDar0tlJqo3tzvyY+ZUSDFW5qzu/gBUn6VJgKqTIPbv14+fYa7mePRRWVj31Vc9JwD16yfhzqag15lr0qTqRIeVK+W955VXgn1WIiKqpKo3IWx63bEjkLnSEBz+p7hYhm4dU+uIfudIh7nCQglxKSnBjZmL96VJAAlz27frYU5VK+vXj+2tyCi81q41XywyK0sqZDbF8L4nREQ1nKrYBLFXo9KpE1AH/3ue/4W57dtl4eH27YG92wzVrkDrzAWrQQMJbZom3xwIb5iLlzYrUHWtOY6Xq5kaNzZfhmPhQlmCwyaGOSKiWKVCSoiVuQz8r8K3fz/KymQDh61bZfWBb2f/LyClpMig40hISNDDiqpE2ZkAoSpW1anNqqquHC9XM91wA3DrrTK2zOWSDznvvAPccYfMBLUp6DYrERFFWBgqcx07An//rzKn7d+P227z3N97wZxDuAiIfIuvWTNZhkCFl5rcZgWqtlkZ5mqWe++V8apnnikfUk45RcaP3nVX4I2XTdgKc2rjZ1+CaPMSEZEZt1sPKdu2yQC3QDslmGjfHvjtf5W5/Zv3Y/JKKQSMHw889BCw7LsIz2RVvJcnCWYCBNusVF24XLKd1l13Sbv1wAFZluPll4FWrWzPYLfVZs3K8n9q0ULWhSMiohAZW4put+eyHjbUqgXUT5Yw594rY+YmTpQwl5MDaJFelkTxXp4kmKVJjh4Fysur3h6PbVbvMMfKXM1QWiqL5/bqJTNXZ8+WEPf77zL1/P/+D7j9dttPa6sy98Ybtp+fiIiCoQKK8tdf8ok9CFkJ8lyZKMb552l44AEXEhKAf/wD2PhqlMNcKG1WQKpzqrqlxGOblWPmaqbx46X61r+/jHe49FLZJeGnn4BnnpGvgxi7ygkQRESxSP2xV0KYBFFHk+dKQgXenHwQCf975z//fCAdEua0aIc5OxMg1LZXgHmrNZ7arMbKnKZxzFxNM3Mm8OabwAcfyHZWFRVSbV65Erj88qAnITHMERHFIrPKXDA0Danl+nNlafsqL595JlA3WcLcAXeUxswF02ZNSNDH1sV7mDOOmdu7V2+nN23q3DFR9GzdKttSAUDnzvJ7ffvtMoYuBAxzRESxyDvMBVuZO3wYLrdb/9qwcHCtWkCv4yQc7dgXoTXmFFV5KiqSqpydCRCA7+VJjBNF4mnMXEmJXqVs0CBya/xRbKmo8PwAk5QUlg8hXJqEiCgWhasy592uNYQ5AOiVewhYAWzalY72wX0Ha445RtqlR47I7Fw7lTlAws7evVUrc8av46EyZ2yzcrxczaNpwDXX6B9ijhwBbryx6geRjz6y9bQMc0REsUiFObX1VYTCXOfWUunatjcd27ZFsNvnckmrdf16CTHBhDmgaphTr5PLFR/VLWOYUy1nLktScwwf7vn1VVeF5WkZ5oiIYpEKKZ06yeDorVtloHSSzbdt7wqfV5jLSJQwdwjp+Owz4J//DPaALWjWTMLc1q32JkAAepvVV5hLT0flzI5YZhwzx8pczROhZUHi4DefiKgGUiGlXTtZLLi8XN/X1I4AlTk1Bu0Q0jFrVhDHaYdxRmuwlTnvMXPxtCwJYD5mjmGOQsQwR0QUi1QIy8rS/9gHMwkiQGXOGObmzat697Ay7gJhdwJEoDZrPIa5zZvlMtusFCKGOSKiWGQMKS1byuVgxs1ZrMzVqp+O0lJg7lz738Iy4y4Q4R4zFw8zWQG9zappwJo1cpmVOQoRwxwRUSwyhrkWLeSyWZj7+29ZiFSNQfMWKMz9Lxy17Spj0mbMCPJ4rQilzepraZJ4a7PWqqUvDFtUJOcMcxQihjkiolhkVpkza7PefTdw2WXA22/7fx7FR2Wu50lS+Zo5E1i7NshjDiQcY+bivc3qcumtVoULBlOIGOaIiGKRCikZGb4rc5oGfPmlXFbjr7ypypyqBvkIc807puP882UN3kcfDe3QfVJjw3bv1o+jpoU5wDPMNWpkfdwgkQ8Mc0REschKZW7DBlmAF6ga0hQV5nJyzO+n2pbp6ZgwQS6++26EqnP16unt0g0b5NzuDhDxPmYO0MfNAWyxUlgwzBERxSJfYc64NdeCBfrlffv8P4+qivkJc8cfD5x3XgSrcy6XHl5UmKtpS5MAnpU5hjkKA4Y5IqJYpCpqderImKrERODoUWDHDv0+VsKceh41LstPmAPgUZ1bt87zruvXA5dfDnzwgb0fxYMKlUeOyHlNb7NyWRIKA4Y5IqJYZAwpSUn6H31jq9VOmLNQmQOAnj3Nq3Nz5gAnnCCzXW+5xbNAaIt3JSpcO0DEU5uVlTkKM4Y5IqJY5F1x8p4EsWmTZ7Cz2mYtLpaJE4pXmAP06tw778jYufx84Jxz9G+xYwfw0092f6D/CTbMVac2K8fMxZ6XXpLhDGlpQJ8+wJIlvu/70UdAr15A3bryIaJ7d+Ctt6J0oOYY5oiIYpFxNitQdRKEqsqpkBNoAoRqs1ZU6AEI0CtdhjBnrM716wfcd5/kv5EjgUsukft8+KH5tysrA8aO9b1SSpXwUtN2gADYZo01M2bIL+2ECcDy5UC3bsCAAcDOneb3r18fuP9+YNEi4NdfgREj5KRmljuAYY6IKNa43VUrTt6VORXmBgyQ80Bt1uzsqsuTuN2mYQ7Qq3N//y1bw778spyuuEKu//BDzwKfMn068OyzwD//6WMdY+/wUtN2gADYZo01kyYBN9wggSw3F5gyRf4/vP66+f1POw0YPBjo1Alo0wa49Vaga1dg4cKoHrYRwxwRUaw5fFhPSirMeVfm5s+X8wsukPP9+80HshkrfFlZ+n0BfRICoIel/+nZE7jjDuD44yU3jhwp1w8cKH/nNm2SIoa3f/9bzg8dAr7/3uRnC3XMnHebNZ4rcy4XFwyOkJKSEhQXF1eeSn3tkFJWBixbBvTvr1+XkCBfL1oU+BtpGjBvnoxHOOWU8Bx8EBjmiIhijaqmuVx6yDLuz7p1qyztkZAgg9kA+aPivXWX8bnq1Kka5ozByCvMAcDTT8vfubw8/br0dGDQILns3WpdtgxYvFj/evZsk58t1DFz3pW5eB4z17ixlD0p7HJzc5GVlVV5ys/PN7/j7t0y9CA72/P67GygsND3N9i/X37nUlKAc88FXngBOOus8P0ANjHMERHFGmO1yeWSy6rNummTXpU7/njPHQTMxs35q8ypMJeaqrdgLVDj5j74wLPVOnmynKv1ib/4wuTBmZmewSvUMKfay/EU5urVk/PmzZ09jmqsoKAA+/fvrzyNGzcuvN8gIwNYsQJYuhR47DEZc6f+XzqAYY6IKNaYtQ6bNZNgd+SIbKAKAKeeKud168q597i50lJZmw7wDHPqfiYzWa0491zJf3/8Afz2m1y3d6+sTQcAr74q2XDNGmDjRq8HGxcOBkLbAWLvXtnnFQDat7f1MzhqwAAZnzVxotNHUm1lZGQgMzOz8pTq6/esQQP5ZS0q8ry+qEgqp74kJABt28pM1jvukE84vqp/UcAwR0QUa8zCXEoK0KSJXFb9y0Bhzth2rV3bd2XOZpjLyADOPlsuq1brtGmSs7p2lc5v375yvWl1zhjmQlma5Oef5bx1a5lhGC8yMmRw/cCBTh8JpaTIANF58/Tr3G752ji+IBC328eMn+hgmCMiijXey5IoatxceblUuE4+Wb72rrh5P0+tWrLwsHeY8zGT1YqLL5bzDz+Uv2Nq4sNNN8mhqaF8pmHOOKM1lDarCnMnnGD5uImqGDtWysnTpgGrVwOjRslYzBEj5PZhwwBjmzY/H5g7V8atrl4NPPOMrDN31VXOHD+AJMe+MxERmfM1Q7NFC+CHH+Ry9+56RS5QZU6FwjBV5gDg/PMlH/72m6zk8Mcf8m2GDpXbBw2Sv3/ffCOd4bQ0w4NDqcypmb4ul4xXAhjmKDRDhgC7dgHjx8ukh+7dZcsTNSli82ZpqyoHD8qnlq1b5feyY0dZWHHIEEcOH2CYIyKKPb7CnKrMAXqLFdDDnPcECONMViCsYa5ePeCMM4CvvpLCBgAMH65/q65dpSu8fTvw3Xd6WxZAcGFOHaOmSTsrLU2vzPXqZfv4iTyMGSMnM94TGx591HOvuxjANisRUazxDmFKoDDnq80aqDJnsiyJFWpWqxoqNGqUfpvLpQ8Jq9JqDaUyB0h1rqhIJj+4XDKrl6gGY5gjIoo1/tqsihovBzjSZgWACy/Uu0+nnSaL5xv5HDcXzJi55GT9mx0+rLdYO3asOraQqIZhmCMiijW+wtwJJ8i6chdeCBxzjH69rwkQEWyzAkDDhnr17bbbqt7ev7+Mq1u7VsaKV2rTBmjVSn6eBIt/hlwuz+VJOPmBqBLDHBFRrPEV5urVkwHa3lsvhNpmDTLMATLue/FifVcxo6wsH0uUpKbKInQ//mjvmxmXJ+HkB6JKDHNERLHG19IkgFSovKtZVidAeN8vhKVJlHr1gN69fd+utv6q0mpNSZGynR3GGa2c/EBUiWGOiCjW2N083qExc1aocXNqiZKQqDC3di2wc6eEwe7dQ3xSovjHpUmIiGKN3TAXaNFg7zBXXCxLfEQhzHXpAjRtCmzbJnM2mjaVal79+lK169/fxpOp4/zuO/3JPRawI6qZWJkjIoo1vpYm8SVQZc57AkRFhSx8GoUw53IBl14ql3/+Gfj0U2DqVGDSJFl7bsYMG0+mKnMqzLHFSgSAlTkiotgTbJt1/359dwTj86jKXHq6bCpeUSH3DXGdOaueekom4O7aBezZI6fFi4FPPgGuvlom5lqq0KnjXLdOzjn5gQgAwxwRUewJNsyVl0tAq11bvvYeM+dySXVuzx7PMBfByhwgS8QZ1zgGJE9ecQUwcyYweDCwYIGFtX+9j5NhjggA26xERLHHbphLT9dnhhpbrWbtWuMkiCiFOTOJibI3+emny487aBDw55/+H1ORaqggpqUBxx0X2YMkihMMc0REscbf0iRmVMUN8AxzZs9jDHNhWJokFKmpwMcfA926yeTUAQPk3Jf1W/UwV9Glu5T8iIhhjogopmia/cocYD4JIoYrc8bD+eIL2Xb2zz+BZ57xfd9te/Qw9/X+XtC0yB8fUTxgmCMiiiWHD6MypQQT5owLB3uPmQNiLswBQE4OMHGiXF60yPf9tvytH+c7607A5MmRPS6ieMEwR0QUS1QAc7nszTI1q8wFarPGSJgD9LkMy5fL5Ahvhw4BW//WX4+lOAG33SazYolqOoY5IqJYogJY7drWN6EHqoa58nJ9y4UYbrMqHTrIj3zwoGzb6m3VKuAQJMxpdeqg8+D2OHpU1rDbvTvKB0sUYxjmiIhiSTDj5YCqEyBUhQ8wr8zt2xe1deasSEwEevaUy2rbVaPly4FDkNDp6tkT/3kjEe3aAVu2AEOHAm53FA+WKMYwzBERxZJgw5x3ZU49T0qKnBRjmFOVuxiozAH6hg5Ll1a9bflyYDbOwZYmvYFbbkFWFvDhh5JDv/oKeOON6B4rUSxhmCMiiiV2lyVRvCdA+NoSTIW5wkL9uhgLc74qc+vQAUtfWAxcdBEA2Zr10Ufl9vvu85z7QVSTMMwREcWScFXmzGayAnqY27FDvy4G2qyAPglixQrg6FH9+tJSGTMHVN0lYswYGW+3cyfw8MNROUyimMMwR0QUS8LdZg0U5tLS7E20iKA2beTwSkuB337Tr//9dwl39eoBLVp4PiYlBXjuObn8/PPA6tVRO1yimBEb/4OJiEj4ao8G4msChK826969ch4jLVZAVmMxa7UuXy7nxx8v9/E2cCBw3nkygff228HFhKnGYZgjIooloVbm1MCxQJU5JYbCHKC3Wn2FOV8mTZIq3ZdfAp99FrnjI4pFMRHmXnpJtnJJSwP69AGWLPF936lT5ZOZ8ZSW5nkfTQPGj5dVxWvVAvr3B/74I5I/ARFRmIR7zJyvypwSY2HObEarlTDXti0wdqxcvv12adUS1RSOh7kZM+Q/4IQJ8h+2W7fAmy1nZspwD3XatMnz9qeekrETU6bI6uC1a8tzqln4REQxK1oTIJQYmfygqMrcqlXynl1eDqxcKdf5C3OAzGjNyZE9XtU4OqKawPEwN2kScMMNwIgRQG6uBLD0dOD1130/xuUCGjfWT9nZ+m2aJv+JH3gAuOACoGtX4M03ge3bgU8+ifRPQ0QUolCXJiktlRTk63lq15YVepUYq8w1awY0bCgh7tdfZTeII0fkx2jb1v9jMzKAJ56Qy88+6zkjlqg6czTMlZUBy5ZJG1RJSJCv/W22fOCAzGhq1kwC2++/67dt3CjLJxmfMytL2re+nrO0tBTFxcWVpxLjyulERNEUbGWuTh19dsC+fb7brC6XZ3UuxsKccRLE0qV6i7VHD2uTbq+4Qj7gFxUBn38eueMkiiWOhrndu2VDZWNlDZCvjetZGnXoIFW7Tz8F3n5btnDp2xfYulVuV4+z85z5+fnIysqqPOXm5gb/QxERhSLY2awJCZ77rvpqswIxHeYAz0kQVsbLGSUnA8OHy+XXXgv/sRHFIsfbrHbl5QHDhgHduwOnngp89JGU5F9+OfjnHDduHPbv3195KigoCNvxEhHZEmxlDvAcN+evXRvjYc64PIndMAcA114r57NnA9u2hffYiGKRo2GuQQMZulFU5Hl9UZGMhbMiOVnK7+vXy9fqcXaeMzU1FZmZmZWnDLtjVYiIwiVcYc5fhS9OwlxBgQzFAeyFuQ4dgJNPls7N1KlhPzyimONomEtJAXr2BObN069zu+XrvDxrz1FRIbOecnLk61atJLQZn7O4WGa1Wn1OIiLHhDvMxWFlLicHaNpU/h4cOiQTbjt0sPcc118v56+/Ls9DVJ053mYdOxZ49VVg2jTZhmXUKODgQZndCkhLddw4/f4PPwx89RWwYYOU36+6SpYmUf9xXS7gtttk8+VZsyToDRsGNGkCXHhhtH86IiKbQglzxl0g4rjNCujVOUCWrEpKsvf4Sy6RZaw2bADmzw/roRHFHMfD3JAhwNNPyyK/3bvLBstz5ugTGDZv9twPeu9eWcqkUyfgnHOk6vbjj7KsiXL33cDNNwMjR8pA2gMH5Dm9FxcmIoo5wS5NAnjuAmG1zRpj68wpahIEYK/FqqSnA1deKZf/85/wHBNVY3Z2L3j1Venj16snp/79/d8/ChwPcwAwZoxU10pLpR3ap49+2/z5nmMenn1Wv29hoUw979HD8/lcLqngFRbK+kRffw20bx+Nn4SIKASaxjbr/xgrc8GEOQC47jo5/+gjYM+e0I+Jqim7uxfMny9r4Hz7rax51qwZcPbZjs62iYkwR0REAA4f1gd4hWs2axxOgABkPLUSbJjr2VP+LpeWAu+8E57jomrI7u4F77wD3HSTtBM7dpTSrxrw7xCGOSKiWKECGBBcyFJhbs8eGXwMxG1lrkED4I47ZFx0167BPYfLpVfn/vMfKXwSeQh29wKjQ4dku5H69SNzjBYwzBERxQoV5mrXtrbdgTcV0rZv16+L0zAHyHjqt97y3H3MrqFDgdRU2Rps6dLwHRvFtpKSEo+dnUpLS83vGMzuBd7uuUdmWRoDYZQxzBERxYpQxssBemVuyxY5T0gwn/kVJ2EuHOrXBy67TC4/+6yzx+LPzJnA1Vd7FmcpeLm5uR47O+Xn50fmGz3xBDB9OvDxx47OsmSYIyKKFeEKc2ogdkaGvl+rUQ0Kc4CMbQckMP31l6OHYuq772Q8/dtvSyag0BUUFHjs7DTOuMaZUSi7Fzz9tIS5r74KfixAmDDMERHFilCWJQH0MHf0qP/nqWFhrnt36YBVVADPPef00Xjatk0qhxUV8vVvvzl7PNVFRkaGx85Oqamp5ncMdveCp54CHnlE1j0zTr12CMMcEVGsCFdlTvH1PHGwzly43XWXnP/nP7JeaSwoK5PFjYuK9EWRf//d2WOqkezuXvDkk8CDD8ps15YtZWxdYaGjPXKGOSKiWOFvoV8rjCENYGXO4KyzpBN28KCsPBELbr8d+OknyeAvvyzXMcw5wO7uBZMn60k8J0c/Pf20E0cPALC5QQoREUVMqJW5zEzPr32Fudq1pb1UVhZ8SzfOuFzAnXdKkeX556UY46vzFg1vvgn8+99y+e239cXy//pLfg2C/RWgII0ZIycz3vvBxeDAS1bmiIhiRahhLinJM5z5eh6XSxZKve8+2dG+hhgyRH7cwkJnFxH+80/gn/+UyxMmAOeeK+PwVSGooMC5Y6P4xDBHRBQrQg1zgOe4OX9Vt9GjgcceC/77xKGUFOC22+Ty00/rm21E2+zZstXkiSdKZ0857jg5Z6uV7GKYIyKKFaHOZgU8x8OxV1fFyJHSjV69GvjiC2eOYeVKOe/f33NtaIY5ChbDHBFRrIhmZa6GysyUQAdIp9kJK1bIeffuntd37iznXJ6E7GKYIyKKFQxzUaHGuc+fL9vYRlN5uR7WunXzvI2VOQoWwxwRUawIdWkSwDPMsc1qqkULqYK53bJ4fzStXQuUlso/TevWnrepMLd1K7B/f3SPi+IbwxwRUaxgZS5qBg6U8zlzovt9VYu1WzfP8XKA/NM1aSKXOaOV7GCYIyKKFeEIc8YJEAxzPg0aJOdz5kR3VqsxzJnhuDkKBsMcEVGsCHdljm1Wn046SV6eoiI9YAVy9CiwYAHwyy/BbwmmZrJ6T35QOG6OgsEwR0QUK8KxNAnbrJakpABnnimXrS5R8swzwGmnAccfD9SvLy919+7APfcAmhb48ZrmeyarwjBHwWCYIyKKFazMRZXdcXOzZsm5eln375dK21NPAevXB378jh3Arl0yVk61U70xzFEwGOaIiGKBpoV/Nisrc36pcXOLFgH79vm/74EDwNKlcvnXX4GDB2WSQm6uXGdljJtqsXboANSqZX4f9Xw7dkR/2RSKXwxzRESx4MgRfSQ+J0BERYsWQKdOQEUFMHeu//v++KOsEde8OdCyJZCeLo/t1UtutxLmArVYAVnUuHlzuczqHFnFMEdEFAtUixWQpBAstlltUdW5QOPmvv1Wzk8/HXC59OvtzD4NNJNVYauV7GKYIyKKBSrMpacDiYnBP0+jRkBSkgS52rXDc2zVmHHcnL9JDPPny/lpp3lebyfMBZrJqjDMkV1JTh8AEREhPJMfAGmzfvqphELvVWmpilNOkZdqxw4ZC2dWNTOOl/MV5tatk50dUlPNv8/Bg3IfIHCY41pzZBf/pxMRxYJwLEuinHNO1dRBplJTgTPOkMu+Wq0LF8q4upYt5WR07LEyzq28XA9rZlatkspfdrac/GFljuximCMiigXhqsyRbYHGzflqsQIyfs5KJc1qixWQiRWALGOya1fg+xMxzBERxQK1NkZmpqOHUROpcXM//GC+wb0Kc6efbv54K2HOykxWpXZtoFUruczqHFnBMEdEFAv+/lvOGzRw9jhqoNatgfbtpZU6e7bnbSUlwM8/y2VfnWs7YS7QTFbv52SYIysY5oiIYsHu3XJ+zDHOHkcNddllcj5xIlBWpl+vxsu1bq2v/+YtUJirqJAxc4C1yhygj5vjJAiygmGOiCgWqDDHypwj7rxTVnVZtw54/nn9en/j5RQV5jZskFmr3v78U66vVUsqgFaoMPfjj/rGIES+MMwREcUChjlHZWUB+fly+eGHgcJCuawWC/YX5ho2lCAIyBZf3lSLtXNn60sI9u0LJCfLcim5ucAnn1h7HNVMDHNERLGAY+Ycd801sj1XSQlw331AcTGwbJncFmilF3+tVjszWZXWrWX8XuvWwNatwODBwIUXAlu2WH8OqjkY5oiIwu3774GPP7b3GI6Zc1xCAvDCC3L5jTeASZNku9w2bYBmzfw/1l+YszOT1ah/f3m+++6TTT0+/VSWLVmwwN7zUPXHMEdEFE7l5cB55wEXX6z36qxgmzUmnHgicPXVcvmhh+Tc15IkRr7C3JEjwKJFctlumANknN1jj0kgzMuTsXeXXw4UFdl/Lqq+GOaIiMJp/XpZrEzTgM2brT+OYS5mPPGE57a2VjbT8BXmPvwQ2LtXdoro3Tv4YzruOODrr+W8sBC46iqZJUsEMMwREYWXWoMCAHbutPaY0lJ9BwiGOcc1aQI88ID+tZUwp2afbt8O7NmjX//yy3J+ww3SKg1Fejrw/vty/vXXwOOPh/Z8VH0wzBERhZMxzFnthanJD4mJMq2SHHf77cBFFwGjRwNNmwa+f2Ym0KKFXFYL/f7+uwyfTEwErrsuPMeVmwtMniyXJ07UZ9vGE01z+giqH4Y5IqJwCqYyZ5z84HKF/5jIttRUaZG++KL1x3i3Wl95Rc7PO89aILRq2DBgxAiZnHHllfE3fm7CBKBePX1MIoWOYY6IKJyCqcxxvFy1YAxzhw4B06bJ1zfeGP7v9eKL+vi54cPjq9pVWChbEfNzS/gwzBERhcvBg7INgGK1MqfarFyWJK4Zw9yMGTIPplUr4Kyzwv+91Pi51FTgyy9lDF28UJ9x1ELLFDqGOSKicPn9d88SCStzNYoxzE2ZIpdHjpT16yIhN1ev+k2YED/VOfXfIjvb2eOoThjmiIjCRbVYU1Pl3O6YOYa5uNaxowS3PXuAJUtkO64RIyL7Pe+5B0hLk7Xsvvoqst8rXGIyzL30EtCypbyYffrIP6Avv/8u60i2bCm94ueei9JB+sYwR0QULirM9esn56zM1ShpaUC7dvrXF10U+cCSkwOMGiWX46E6p2kxGOZmzADGjpUXcPlyoFs3YMAA3x/GDh2SfdaeeAJo3Di6x+oDwxwRUbioMNe/v5zv3m1tZVeOmas2VKsVAP75z+h8z3vukZ0iFi8G5swJfP99++Qxa9ZE/NCqOHgQOHxYLsdMmJs0SRYCHDFCetdTpsigxNdfN7//CScA//qXbMWhqvAOY5gjIgoHTQN+/VUun3GGtF80Ta+6+cPKXLWhwlz79tYWGw6H7GzgppvkspXq3BNPAE89BVx2WfR3kVBVufR0oE6dyH2fkpISFBcXV55KS0vN71hWBixbpn8AA6RX3r+/vg9bHGCYIyIKh6IiCWUuF9Cli15ls9JqZZirNq67Djj7bBmCFc2lN+6+W6pzS5cCs2f7vp+mSVcRkELyu+9G5/iUaLVYc3NzkZWVVXnKz883v6OqnnsfUHa2vb2VHcYwR0QUDqrF2ratlB3UHwcrkyAY5qqNZs1kqRBjoScaGjWS3SoA2RnCV3VuyRLgr7/0rx94ADhyJNJHp4tWmCsoKMD+/fsrT+PGjYvsN3QYwxwRUTioMNeli5yrv1ZWKnMcM0dhcNdd8jni55+Bzz4zv8/06XI+eLDsSrF5s749WDREK8xlZGQgMzOz8pTqa2xbgway35r3/9OiopiZ3GAFwxwRUTh4hzm1ImqgytyRI8CBA3KZlTkKQaNGwJgxcvnRR6tW59xuWWgYkLH+ajutRx+VBY7D4cAB4Isv5HuZibmZrCkpQM+ewLx5+nVut3ydl+fccdnEMEdEFA7BVuZUVS4xEcjKisyxUY0xdqwskbJkCfDNN563LVwIbN8uv2Znny3bgHXqJOviPfVUeL7/pZcC55wDfPCB+e0xF+YAedFefVX2X1u9WtZ6OXhQXyRw2DDA2KYtKwNWrJBTWRmwbZtcXr/egYMXDHNERKGqqJCFRAH7lTnjeDluVkkhys4Grr9eLj/+uOdtauLD4MGyokZSEqDmBTz7rAS9UHz7rb40iprY7S0mw9yQIcDTTwPjxwPdu0swmzNHP8jNm4EdO/T7b98O9Oghpx075LE9eugvvAMY5oiIQvXnn9IurVULaNNGrrNbmeN4OQqTu+6SoPbNN8BPP8l15eXAzJly+fLL9fuefz7Qt6+s/abarsHQNOD++/WvN282v19MhjlA+tObNgGlpbJgX58++m3z5wNTp+pft2wpP7D3af786B6zAcMcEVGoVIv1uOOkXQoEV5kjCoPmzYGrr5bLqjo3fz6wa5d8ZjjjDP2+Lhfw5JNy+T//kXXqfC3J5s/nn3suyxZ3YS7OMcwREYXKe7wcYL0yxzBHEXDPPRLU/vtfaXmqFusll8iesUYnnQTceKOM+3/4YeD44/WKnhVut16VO+kkOd+yxfy+DHORwTBHRBQqszBnrMz5W5JfhTm2WSmMOnSQ4AZIQPvwQ7k8ZIj5/f/9b2nDNmoEFBRI6/XWW/WJ1v68/74ExsxM4IUX5LotW6rOaD18GCgpkcsMc+HFMEdEFCp/Ya60FCgu9v1YNWaOlTkKs/vuk/MPPwT27pVl0045xfy+LpeEv9WrgWuukc8fzz8vW5Lt2+f7exw9Cjz4oFy+6y7ZziwhQa43W7oNkMkXmZkh/GBURUyEuZdekvGEaWky5nDJEmuPmz5dfgEvvNDz+muukeuNp4EDw3zQRESALGGgliQwhjnj5pP+Wq1ss1KEdO8uy4Qol16qD+n0pX594I03ZBeLBg1k29JzztErat6mTZNf/4YNpZKXlAQ0aSK3eY+bM7ZYOXE7vBwPczNmyBIvEyYAy5cD3boBAwYEHjP811/AnXcCJ59sfvvAgTJjWJ3eey/sh05EJD0pTZO/Zmb7OwL+39AY5iiCVHUO8N1iNXP22cDXXwP16snEhvPOAw4d8rzPr7/qM2Dvuw/IyJDLzZvLufe4OY6XixzHw9ykScANN8jafLm5wJQp8oH29dd9P6aiAhg6VH6JWrc2v09qqpSU1alevcgcPxHVcGYtVsXKJAguTUIR1K+fFEvGjrW/oUG3bsBXX0lLdMECWZ/uyBGZHHHeeXL71q2yH+2NN+qPU2HOX2WOwsvRMFdWJiVc44bECQnytXGKs7eHH5bhKNdd5/s+8+fLfTp0kMWc1fslEVFYeS8WbGRleRJW5ijCJk4EnnlG/r7a1auXbM9Vu7YEuzZtJBR+9pm0SocMkfXs0tL0xzDMRV+Sk998926pspl1JtasMX/MwoXAa6/JAs2+DBwIXHQR0KqVrOV5333AoEESEM3GC5SWlqLUsLBOia/BAURE3tSbVadOVW+zUpljmKMY17evhLdBg2Tzg6Qk2eHqnnuA9u2r3r9ZMzn3FebUZxwKH0fDnF0lJbIQ4quv+n/fM65u3aUL0LWrfJqYPx8488yq98/Pz8dDoSx9TUQ119q1ct6hQ9XbAlXmjhyRCRQAwxzFtNNOk7+hX38tf4dV9c2MrzFz6r8BK3Ph52iYa9BAKmVm05cbN656/z//lIkP552nX6fWsUlKkvdUtZOOUevW8r3WrzcPc+PGjcPYsWMrv962bRtyc3Pt/0BEVLOUlgIbN8plszAXqDKnxn8kJXGtBop5ffp47nLlC9us0efomLmUFKBnT2DePP06t1u+Nhuo2bGjjDVesUI/nX8+cPrpclmVdr1t3SrvmTk55renpqYiMzOz8pShpuQQEfmzfr28aWVmmn8CDVSZMy4YzLUaqJpQf4t37pTis8IwFzmOt1nHjgWGD5dBlr17A889J12HESPk9mHDgKZNgfx8GWDZubPn4+vWlXN1/YEDMsv14ovlvfXPP4G77wbatpUlT4iIwsbYYjULY4EqcxwvR9VQ/fqyKsWhQ1JMadtWrmeYixzHw9yQIbL57/jxQGGhLHI4Z47+j715s70ZOImJsvbNtGmyanWTJrJeziOPyHIlRERhoyY/dOxofrudyhxRNeFySat1zRr5G962raxesXev3M4wF36OhzkAGDNGTmbmz/f/2KlTPb+uVUtWriYiijh/kx8A/a/W/v3SbzKu3wBwKy+qtoxhDtA/zyQlcd3XSHB80WAiorgVqDJXty6QnCyXzapzbLNSNeW9PIlxWZJg1rsj//iSEhEFQ9MCV+ZcLv+tVoY5qqa8lyfheLnIYpgjIgpGUZG0TxMS9BHeZvxNguCYOaqmvJcnYZiLLIY5IqJgqKpcy5ZVx8IZ+avMccwcVVO+2qwMc5HBMEdEFIxA4+UUK5U5hjmqZoxtVk1jmIs0hjkiomAEGi+ncMwc1UDHHivnBw/KkiQMc5HFMEdEFAxVmQsU5jhmjmqgWrX0zzGbNzPMRRrDHBFRMFRlLlCb1Vdl7vBhWSIfYGWOqiXjuDmGuchimCMisuvIEWDjRrkcbGVOTX5ISpK9XYmqGeO4OYa5yGKYIyKya/16GdWdlRX4r5OvypxxvJzZvq5EcU6FuQ0b9M8uDHORwTBHRGSXcbxcoCCm/nrt2gVUVOjXc7wcVXOqzbpsmXz2SUjgr3ukMMwREdlldbwcoI+Hc7v18gTANeao2lOVueXL5bxhQyAx0bnjqc4Y5oiI7LI6kxWQvVlVOcLYauWyJFTNqTBXUiLnbLFGDsMcEZFddipzgPkkCLZZqZpTYU5hmIschjkiIjs0zV5lDjCfBMHKHFVz2dlSmDZ+TZHBMEdEZEdhofSNEhKAtm2tPcasMscxc1TNJSToO0EA+mcaCj+GOSIiO1RVrlUrIDXV2mNYmaMaythqZWUuchjmiKh6KS8HJk4ELrxQFrgKN6t7shqpv2KvvAJ07y6nhQvlOo6Zo2pMLU8CMMxFEsMcEVUfe/YA55wDPPQQ8OmnwAknAPPmhfd7qMqc1ckPANC1q5z//TewcqWcDh+WPlSnTuE9PqIYEjeVuZdeAlq2BNLSgD59gCVL/N9/5kx5D0hLA7p0AWbPjsph+sIwR0TVw2+/SXibOxdIT5c32D17gAEDgOeek4kL4RBMZe4f/wB+/hn48kvP0+rV0q4lqqbiIszNmAGMHQtMmCCL4nXrJu8b3ru2KD/+CFxxBXDddcAvv0gX4MIL5T3IIS5NC9c7XPWxdetWNGvWDFu2bMGxxtGbodq/H9i3L3zPR0Tip5/kjfXgQfl0/emnQPv2wD//Cbz5ptxn+HB5s04I8TPsKafIzuELFshlIvLpiy+kWA4A27YBTZpE9vsF9fe7Tx/5IPjii/K12y394ZtvBu69t+r9hwyR95rPPtOvO/FEGT4xZUrIP0Mwkhz5rjXV5MnAuHFOHwVR9XXGGfIpW00qmDoV6NEDuOMOYNo0OYWLncocUQ1lHDPXsGH0vm9JSQmKi4srv05NTUWq2YSlsjLZb8z4tzkhAejfH1i0yPzJFy2SSp7RgAHAJ5+EfuBBYpiLpqQk6a8TUXglJwMjRwJPPCH/zxSXC7jtNqBzZ6nSbd8enu83cCDXWSCyoGNH4OSTgRYtPNeci7Tc3FyPrydMmICJEydWvePu3bJnsncPODtbHx/rrbDQ/P6FhcEfcIgY5qLpzjvlRETR1b8/8OefTh8FUY2TlAR89130v29BQQGaNm1a+bVpVa4aYZgjIiKiaiUjIwOZmZmB79igAZCY6LmgNyBfN25s/pjGje3dPwo4m5WIiIhqppQUoGdPzyWM3G75Oi/P/DF5eVWXPJo71/f9o4CVOSIiIqq5xo6V2e69egG9e8tSRgcPAiNGyO3DhgFNmwL5+fL1rbcCp54KPPMMcO65wPTpsvTQK6849iMwzBEREVHNNWQIsGsXMH68TGLo3h2YM0ef5LB5s+eSRn37Au++CzzwAHDffUC7djKTtXNnJ44eANeZMxWxdeaIiIgoYmrq32+OmSMiIiKKYwxzRERERHGMYY6IiIgojjHMEREREcUxhjkiIiKiOMYwR0RERBTHGOaIiIiI4hjDHBEREVEcY5gjIiIiimPczsuE2+0GAOzYscPhIyEiIiKr1N9t9Xe8pmCYM1FUVAQA6N27t8NHQkRERHYVFRWhefPmTh9G1HBvVhPl5eX45ZdfkJ2djYSE8HaiS0pKkJubi4KCAmRkZIT1uckTX+vo4WsdPXyto4evdfSE67V2u90oKipCjx49kJRUc+pVDHNRVlxcjKysLOzfvx+ZmZlOH061xtc6evhaRw9f6+jhax09fK1DwwkQRERERHGMYY6IiIgojjHMRVlqaiomTJiA1NRUpw+l2uNrHT18raOHr3X08LWOHr7WoeGYOSIiIqI4xsocERERURxjmCMiIiKKYwxzRERERHGMYY6IiIgojjHMRdFLL72Eli1bIi0tDX369MGSJUucPqS4l5+fjxNOOAEZGRlo1KgRLrzwQqxdu9bjPkeOHMHo0aNxzDHHoE6dOrj44osrt2yj4D3xxBNwuVy47bbbKq/jax0+27Ztw1VXXYVjjjkGtWrVQpcuXfDzzz9X3q5pGsaPH4+cnBzUqlUL/fv3xx9//OHgEceniooKPPjgg2jVqhVq1aqFNm3a4JFHHoFxbiBf6+B89913OO+889CkSRO4XC588sknHrdbeV337NmDoUOHIjMzE3Xr1sV1112HAwcORPGniA8Mc1EyY8YMjB07FhMmTMDy5cvRrVs3DBgwADt37nT60OLaggULMHr0aPz000+YO3cujh49irPPPhsHDx6svM/tt9+O//73v5g5cyYWLFiA7du346KLLnLwqOPf0qVL8fLLL6Nr164e1/O1Do+9e/eiX79+SE5OxhdffIGCggI888wzqFevXuV9nnrqKTz//POYMmUKFi9ejNq1a2PAgAE4cuSIg0cef5588klMnjwZL774IlavXo0nn3wSTz31FF544YXK+/C1Ds7BgwfRrVs3vPTSS6a3W3ldhw4dit9//x1z587FZ599hu+++w4jR46M1o8QPzSKit69e2ujR4+u/LqiokJr0qSJlp+f7+BRVT87d+7UAGgLFizQNE3T9u3bpyUnJ2szZ86svM/q1as1ANqiRYucOsy4VlJSorVr106bO3euduqpp2q33nqrpml8rcPpnnvu0U466SSft7vdbq1x48bav/71r8rr9u3bp6WmpmrvvfdeNA6x2jj33HO1a6+91uO6iy66SBs6dKimaXytwwWA9vHHH1d+beV1LSgo0ABoS5curbzPF198oblcLm3btm1RO/Z4wMpcFJSVlWHZsmXo379/5XUJCQno378/Fi1a5OCRVT/79+8HANSvXx8AsGzZMhw9etTjte/YsSOaN2/O1z5Io0ePxrnnnuvxmgJ8rcNp1qxZ6NWrFy699FI0atQIPXr0wKuvvlp5+8aNG1FYWOjxWmdlZaFPnz58rW3q27cv5s2bh3Xr1gEAVq5ciYULF2LQoEEA+FpHipXXddGiRahbty569epVeZ/+/fsjISEBixcvjvoxx7Ikpw+gJti9ezcqKiqQnZ3tcX12djbWrFnj0FFVP263G7fddhv69euHzp07AwAKCwuRkpKCunXretw3OzsbhYWFDhxlfJs+fTqWL1+OpUuXVrmNr3X4bNiwAZMnT8bYsWNx3333YenSpbjllluQkpKC4cOHV76eZu8pfK3tuffee1FcXIyOHTsiMTERFRUVeOyxxzB06FAA4GsdIVZe18LCQjRq1Mjj9qSkJNSvX5+vvReGOao2Ro8ejd9++w0LFy50+lCqpS1btuDWW2/F3LlzkZaW5vThVGtutxu9evXC448/DgDo0aMHfvvtN0yZMgXDhw93+Oiql/fffx/vvPMO3n33XRx33HFYsWIFbrvtNjRp0oSvNcUNtlmjoEGDBkhMTKwyq6+oqAiNGzd26KiqlzFjxuCzzz7Dt99+i2OPPbby+saNG6OsrAz79u3zuD9fe/uWLVuGnTt34vjjj0dSUhKSkpKwYMECPP/880hKSkJ2djZf6zDJyclBbm6ux3WdOnXC5s2bAaDy9eR7Sujuuusu3Hvvvbj88svRpUsXXH311bj99tuRn58PgK91pFh5XRs3blxlkmB5eTn27NnD194Lw1wUpKSkoGfPnpg3b17ldW63G/PmzUNeXp6DRxb/NE3DmDFj8PHHH+Obb75Bq1atPG7v2bMnkpOTPV77tWvXYvPmzXztbTrzzDOxatUqrFixovLUq1cvDB06tPIyX+vw6NevX5UldtatW4cWLVoAAFq1aoXGjRt7vNbFxcVYvHgxX2ubDh06hIQEzz+FiYmJcLvdAPhaR4qV1zUvLw/79u3DsmXLKu/zzTffwO12o0+fPlE/5pjm9AyMmmL69OlaamqqNnXqVK2goEAbOXKkVrduXa2wsNDpQ4tro0aN0rKysrT58+drO3bsqDwdOnSo8j433nij1rx5c+2bb77Rfv75Zy0vL0/Ly8tz8KirD+NsVk3jax0uS5Ys0ZKSkrTHHntM++OPP7R33nlHS09P195+++3K+zzxxBNa3bp1tU8//VT79ddftQsuuEBr1aqVdvjwYQePPP4MHz5ca9q0qfbZZ59pGzdu1D766COtQYMG2t133115H77WwSkpKdF++eUX7ZdfftEAaJMmTdJ++eUXbdOmTZqmWXtdBw4cqPXo0UNbvHixtnDhQq1du3baFVdc4dSPFLMY5qLohRde0Jo3b66lpKRovXv31n766SenDynuATA9vfHGG5X3OXz4sHbTTTdp9erV09LT07XBgwdrO3bscO6gqxHvMMfXOnz++9//ap07d9ZSU1O1jh07aq+88orH7W63W3vwwQe17OxsLTU1VTvzzDO1tWvXOnS08au4uFi79dZbtebNm2tpaWla69attfvvv18rLS2tvA9f6+B8++23pu/Pw4cP1zTN2uv6999/a1dccYVWp04dLTMzUxsxYoRWUlLiwE8T21yaZljmmoiIiIjiCsfMEREREcUxhjkiIiKiOMYwR0RERBTHGOaIiIiI4hjDHBEREVEcY5gjIiIiimMMc0RERERxjGGOiMgHl8uFTz75xOnDICLyi2GOiGLSNddcA5fLVeU0cOBApw+NiCimJDl9AEREvgwcOBBvvPGGx3WpqakOHQ0RUWxiZY6IYlZqaioaN27scapXrx4AaYFOnjwZgwYNQq1atdC6dWt88MEHHo9ftWoVzjjjDNSqVQvHHHMMRo4ciQMHDnjc5/XXX8dxxx2H1NRU5OTkYMyYMR637969G4MHD0Z6ejratWuHWbNmVd62d+9eDB06FA0bNkStWrXQrl27KuGTiCjSGOaIKG49+OCDuPjii7Fy5UoMHToUl19+OVavXg0AOHjwIAYMGIB69eph6dKlmDlzJr7++muPsDZ58mSMHj0aI0eOxKpVqzBr1iy0bdvW43s89NBDuOyyy/Drr7/inHPOwdChQ7Fnz57K719QUIAvvvgCq1evxuTJk9GgQYPovQBERACgERHFoOHDh2uJiYla7dq1PU6PPfaYpmmaBkC78cYbPR7Tp08fbdSoUZqmadorr7yi1atXTztw4EDl7Z9//rmWkJCgFRYWapqmaU2aNNHuv/9+n8cAQHvggQcqvz5w4IAGQPviiy80TdO08847TxsxYkR4fmAioiBxzBwRxazTTz8dkydP9riufv36lZfz8vI8bsvLy8OKFSsAAKtXr0a3bt1Qu3btytv79esHt9uNtWvXwuVyYfv27TjzzDP9HkPXrl0rL9euXRuZmZnYuXMnAGDUqFG4+OKLsXz5cpx99tm48MIL0bdv36B+ViKiYDHMEVHMql27dpW2Z7jUqlXL0v2Sk5M9vna5XHC73QCAQYMGYdOmTZg9ezbmzp2LM888E6NHj8bTTz8d9uMlIvKFY+aIKG799NNPVb7u1KkTAKBTp05YuXIlDh48WHn7Dz/8gISEBHTo0AEZGRlo2bIl5s2bF9IxNGzYEMOHD8fbb7+N5557Dq+88kpIz0dEZBcrc0QUs0pLS1FYWOhxXVJSUuUkg5kzZ6JXr1446aST8M4772DJkiV47bXXAABDhw7FhAkTMHz4cEycOBG7du3CzTffjKuvvhrZ2dkAgIkTJ+LGG29Eo0aNMGjQIJSUlOCHH37AzTffbOn4xo8fj549e+K4445DaWkpPvvss8owSUQULQxzRBSz5syZg5ycHI/rOnTogDVr1gCQmabTp0/HTTfdhJycHLz33nvIzc0FAKSnp+PLL7/ErbfeihNOOAHp6em4+OKLMWnSpMrnGj58OI4cOYJnn30Wd955Jxo0aIBLLrnE8vGlpKRg3Lhx+Ouvv1CrVi2cfPLJmD59ehh+ciIi61yapmlOHwQRkV0ulwsff/wxLrzwQqcPhYjIURwzR0RERBTHGOaIiIiI4hjHzBFRXOIIESIiwcocERERURxjmCMiIiKKYwxzRERERHGMYY6IiIgojjHMEREREcUxhjkiIiKiOMYwR0RERBTHGOaIiIiI4hjDHBEREVEc+3+qJ0O061jbFgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from tqdm import tqdm\n",
    "\n",
    "epochs, lr = 100, 1e-4\n",
    "\n",
    "for i in range(5):\n",
    "    df = pd.DataFrame(columns=['Loss', 'Recall'])\n",
    "    \n",
    "    model_name = f\"{i}-net\"\n",
    "    if InDL:\n",
    "        model_name = f\"{model_name}-InDL\"\n",
    "    else:\n",
    "        model_name = f\"{model_name}-MNIST\"\n",
    "\n",
    "    model = Net(i).to(device)\n",
    "    optimizer = torch.optim.AdamW(model.parameters(), lr=lr)\n",
    "    \n",
    "    model_path = f'./models/{exp_name}/{model_name}.pt'\n",
    "    history_path = f'./models/{exp_name}/{model_name}_history.csv'\n",
    "\n",
    "    if os.path.exists(history_path):\n",
    "        df = pd.read_csv(history_path)\n",
    "    else:\n",
    "        previous_best_recall = 0\n",
    "        for epoch in tqdm(range(1, epochs+1)):\n",
    "            loss = train(model, device, train_loader, optimizer, epoch)\n",
    "            recall = test(model, device, test_loader)\n",
    "            \n",
    "            df.loc[len(df)] = [loss, recall]\n",
    "            \n",
    "            if recall > previous_best_recall:\n",
    "                previous_best_recall = recall\n",
    "                torch.save(model.state_dict(), model_path)\n",
    "                \n",
    "        df.to_csv(history_path, index=False)\n",
    "\n",
    "    # Assuming df is your DataFrame and it has columns 'Loss' and 'Class 0 Recall'\n",
    "\n",
    "    fig, ax1 = plt.subplots()\n",
    "\n",
    "    # Plot Loss\n",
    "    sns.lineplot(data=df, x=df.index, y='Loss', ax=ax1, color='blue')\n",
    "    ax1.set_ylabel('Loss', color='blue')\n",
    "    ax1.tick_params(axis='y', labelcolor='blue')\n",
    "\n",
    "    # Create a second y-axis and plot Recall on it\n",
    "    ax2 = ax1.twinx()\n",
    "    sns.lineplot(data=df, x=df.index, y='Recall', ax=ax2, color='red')\n",
    "    ax2.set_ylabel('Recall', color='red')\n",
    "    ax2.tick_params(axis='y', labelcolor='red')\n",
    "    \n",
    "    ax1.set_xlabel('Epochs')\n",
    "\n",
    "    plt.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0-net-InDL\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.85      0.88       282\n",
      "           1       0.70      0.81      0.75       118\n",
      "\n",
      "    accuracy                           0.84       400\n",
      "   macro avg       0.81      0.83      0.82       400\n",
      "weighted avg       0.85      0.84      0.85       400\n",
      "\n",
      "1-net-InDL\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.72      0.84       282\n",
      "           1       0.60      1.00      0.75       118\n",
      "\n",
      "    accuracy                           0.81       400\n",
      "   macro avg       0.80      0.86      0.80       400\n",
      "weighted avg       0.88      0.81      0.81       400\n",
      "\n",
      "2-net-InDL\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.57      0.73       282\n",
      "           1       0.49      1.00      0.66       118\n",
      "\n",
      "    accuracy                           0.70       400\n",
      "   macro avg       0.75      0.79      0.69       400\n",
      "weighted avg       0.85      0.70      0.71       400\n",
      "\n",
      "3-net-InDL\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.72      0.84       282\n",
      "           1       0.60      1.00      0.75       118\n",
      "\n",
      "    accuracy                           0.80       400\n",
      "   macro avg       0.80      0.86      0.79       400\n",
      "weighted avg       0.88      0.80      0.81       400\n",
      "\n",
      "4-net-InDL\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.83      0.86       282\n",
      "           1       0.65      0.77      0.71       118\n",
      "\n",
      "    accuracy                           0.81       400\n",
      "   macro avg       0.77      0.80      0.78       400\n",
      "weighted avg       0.82      0.81      0.81       400\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "def evaluate_model(model, dataloader, device):\n",
    "    model.eval()  # set the model to evaluation mode\n",
    "    \n",
    "    all_predictions = []\n",
    "    all_labels = []\n",
    "    \n",
    "    with torch.no_grad():  # don't calculate gradients\n",
    "        for inputs, labels in test_loader:\n",
    "            inputs = inputs.to(device)\n",
    "            labels = labels.to(device)\n",
    "            \n",
    "            outputs = model(inputs)\n",
    "            _, predictions = torch.max(outputs, 1)\n",
    "            \n",
    "            all_predictions.extend(predictions.cpu().numpy())\n",
    "            all_labels.extend(labels.cpu().numpy())\n",
    "    \n",
    "    print(classification_report(all_labels, all_predictions))\n",
    "\n",
    "for i in range(5):\n",
    "    model_name = f\"{i}-net\"\n",
    "    if InDL:\n",
    "        model_name = f\"{model_name}-InDL\"\n",
    "    else:\n",
    "        model_name = f\"{model_name}-MNIST\"\n",
    "        \n",
    "    state_dict = torch.load(f\"models/{exp_name}/{model_name}.pt\")\n",
    "    model = Net(i).to(device)\n",
    "    model.load_state_dict(state_dict)\n",
    "    \n",
    "    print(model_name)\n",
    "\n",
    "    # Assuming you have a model, dataloader and device (CPU or CUDA) defined\n",
    "    evaluate_model(model, test_loader, device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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